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Programmable self-assembly of chained modules holds potential for the automatic shape formation of morphologically adapted robots. However, current systems are limited to modules of uniform rigidity, which restricts the range of obtainable morphologies and thus the functionalities of the system. To address these challenges, we previously introduced soft cells as modules that can obtain different mechanical softness pre-setting. We showed that such a system can obtain a higher diversity of morphologies compared to state-of-the-art systems and we illustrated the system’s potential by demonstrating the self-assembly of complex morphologies. In this paper, we extend our previous work and present an automatic method that exploits our system’s capabilities in order to find a linear chain of soft cells that self-folds into a target 2-D shape.
Posted on: May 19, 2014
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Several strategies have been proposed to improve patient motivation and exercise intensity during robot-aided stroke rehabilitation. One relatively unexplored possibility is two-player gameplay, allowing subjects to compete or cooperate with each other to achieve a common goal. In order to explore the potential of such games, we designed a two-player game played using two ARMin arm rehabilitation robots.
Posted on: May 12, 2014
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This paper addresses the local terrain mapping process for an autonomous robot. Building upon an onboard range measurement sensor and an existing robot pose estimation, we formulate a novel elevation mapping method from a robot-centric perspective. This formulation can explicitly handle drift of the robot pose estimation which occurs for many autonomous robots. Our mapping approach fully incorporates the distance sensor measurement uncertainties and the six-dimensional pose covariance of the robot. We introduce a computationally efficient formulation of the map fusion process, which allows for mapping a terrain at high update rates. Finally, our approach is demonstrated on a quadrupedal robot walking over obstacles.
Posted on: May 12, 2014
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This paper presents the application of reinforcement learning to improve the performance of highly dynamic single legged locomotion with compliant series elastic actuators. The goal is to optimally exploit the capabilities of the hardware in terms of maximum jump height, jump distance, and energy efficiency of periodic hopping. These challenges are tackled with the reinforcement learning method Policy Improvement with Path Integrals (PI2) in a model-free approach to learn parameterized motor velocity trajectories as well as highlevel control parameters. The combination of simulation and hardware-based optimization allows to efficiently obtain optimal control policies in an up to 10-dimensional parameter space. The robotic leg learns to temporarily store energy in the elastic elements of the joints in order to improve the jump height and distance. In addition, we present a method to learn time-independent control policies and apply it to improve the energetic efficiency of periodic hopping.
Posted on: May 12, 2014
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Triggered assistance has been shown to be a successful robotic strategy for provoking motor plasticity, probably because it requires neurologic patients’ active participation to initiate a movement involving their impaired limb. Triggered assistance, however, requires sufficient residual motor control to activate the trigger and, thus, is not applicable to individuals with severe neurologic injuries. In these situations, brain and body–computer interfaces have emerged as promising solutions to control robotic devices. In this paper, we investigate the feasibility of a body–machine interface to detect motion execution only monitoring the autonomic nervous system (ANS) response. Four physiological signals were measured (blood pressure, breathing rate, skin conductance response and heart rate) during an isometric pinching task and used to train a classifier based on hidden Markov models. We performed an experiment with six healthy subjects to test the effectiveness of the classifier to detect rest and active pinching periods. The results showed that the movement execution can be accurately classified based only on peripheral autonomic signals, with an accuracy level of 84.5%, sensitivity of 83.8% and specificity of 85.2%. These results are encouraging to perform further research on the use of the ANS response in body–machine interfaces.
Posted on: May 5, 2014
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This article considers the suitability of current robots designed to assist humans in accomplishing their daily domestic tasks. With several million units sold worldwide, robotic vacuum cleaners are currently the figurehead in this field. As such, we will use them to investigate the following key question: How does a service cleaning robot perform in a real household? One must consider not just how well a robot accomplishes its task, but also how well it integrates inside the user’s space and perception. We took a holistic approach to addressing these topics by combining two studies in order to build a common ground. In the first of these studies, we analyzed a sample of seven robots to identify the influence of key technologies, such as the navigation system, on technical performance. In the second study, we conducted an ethnographic study within nine households to identify users’ needs. This innovative approach enables us to recommend a number of concrete improvements aimed at fulfilling users’ needs by leveraging current technologies to reach new possibilities. (C) 2013 Elsevier B.V. All rights reserved.
Posted on: May 2, 2014
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Grasping plays a central role in our daily life. To interact with objects surrounding them, people use a large diversity of hand configurations in combination with forces ranging from the small ones involved in manipulating a pen for writing, to larger forces such as when drinking a cup full of water, and even larger ones such as when wielding a hammer. In this paper we present a setup to capture human hand configuration and motion as well as the forces applied by the hand on objects while performing a task. Hand configuration is obtained through the use of a data glove device while interaction forces are measured through an array of tactile sensors. Current approaches in the state-of-the-art are limited in that they only measure interaction forces on the fingers or the palm, ignoring the important role of the sides of the fingers in achieving a grasp/manipulation task. We propose a new setup for a “sensorized” data glove to address these limitations and through which a more complete picture of human hand response in grasping and manipulation can be obtained. This setup was successfully tested on five subjects performing a variety of different tasks.
Posted on: April 14, 2014
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We implemented single-session workshops using the Thymio- II—a small, self-contained robot designed for young stu- dents, and VPL—a graphical software development envi- ronment based upon event handling. Our goal was to in- vestigate if the students could learn this core computer sci- ence concept while enjoying themselves in the robotics con- text. A visual questionnaire was developed based upon the combined Bloom and SOLO taxonomies, although it proved difficult to construct a questionnaire appropriate for young students. We found that—despite the short duration of the workshop—all but the youngest students achieved the cog- nitive level of Unistructural Understanding, while some stu- dents achieved higher levels of Unistructural Applying and Multistructural Understanding and Applying.
Posted on: April 14, 2014
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Thymio II is a small robot developed for education. It aims at offering a wide public the possibility to understand the basics of robotics and programming. To achieve this, it aims at being appealing to a large age range and serve as a medium for several types of activities. In this study, we tested it in five different workshops of the EPFL Robotics Festival with various activities. The workshops target different age groups and the participants can control the robot via different means: built- in buttons, graphical programming and text programming. At the end of the activities, participants were asked to fill a short survey to give their impressions about the robot, their appreciation of the tasks and their motivations to take part. We could show through this feedback that ThymioII appeals to young children as much as to teenagers, to both girls and boys, and allows them to have fun and learn new things.
Posted on: April 14, 2014
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Hand loss is a highly disabling event that markedly affects the quality of life. To achieve a close to natural replacement for the lost hand, the user should be provided with the rich sensations that we naturally perceive when grasping or manipulating an object. Ideal bidirectional hand prostheses should involve both a reliable decoding of the user’s intentions and the delivery of nearly "natural" sensory feedback through remnant afferent pathways, simultaneously and in real time. However, current hand prostheses fail to achieve these requirements, particularly because they lack any sensory feedback. We show that by stimulating the median and ulnar nerve fascicles using transversal multichannel intrafascicular electrodes, according to the information provided by the artificial sensors from a hand prosthesis, physiologically appropriate (near-natural) sensory information can be provided to an amputee during the real-time decoding of different grasping tasks to control a dexterous hand prosthesis. This feedback enabled the participant to effectively modulate the grasping force of the prosthesis with no visual or auditory feedback. Three different force levels were distinguished and consistently used by the subject. The results also demonstrate that a high complexity of perception can be obtained, allowing the subject to identify the stiffness and shape of three different objects by exploiting different characteristics of the elicited sensations. This approach could improve the efficacy and "life-like" quality of hand prostheses, resulting in a keystone strategy for the near-natural replacement of missing hands.
Posted on: April 2, 2014
